You can copy the layerGraph of the pretrained network and change the imageInputLayer, the first convolutionLayer to match the input image channel dimension & convolution filter dimensions. Then you can freeze/unfreeze the existing pretrained weights during training the new network accordingly.
You can do something like below:(N=50)
imageSize = [224 224 3];
numClasses = 10;
N = 50;
lgraph = deeplabv3plusLayers(imageSize, numClasses, "resnet50");
analyzeNetwork(lgraph)
layers = lgraph.Layers
newlgraph = replaceLayer(lgraph,'input_1',imageInputLayer([224 224 N],'Name','input'));
newlgraph = replaceLayer(newlgraph,'conv1',convolution2dLayer(7,64,'stride',[2 2],'padding',[3 3 3 3],'Name','conv1'))
analyzeNetwork(newlgraph)
Best Answer